Establishing a Decision-Making Support Framework for Optimizing Unmanned Aerial Systems (UAS) Flight Planning to Monitor Heritage Building Defects

Abstract

Due to the excellence in mobility and automation, Unmanned Aerial Systems (UAS, a.k.a. drones) have been widely implemented in detecting building anomalies and assessing construction conditions. Additionally, the AECO industry is expanding significant interest in the preservation of as-built heritage using Building Information Modeling (BIM), generally described as Heritage or Historic BIM (HBIM), which focuses on long-term documentation and real-time monitoring of historical buildings. This research proposes a vision for integrating UAS and HBIM for continuous building-defect monitoring by establishing a decision-making support framework for autonomous drone inspection. Three essential aspects concerning defect monitoring will be presented: a summary of the taxonomy of historical building defects (BDs) for identifying appropriate detecting occasions, the potential to increase the efficiency and adaptability of drone utilization by specifying drone types based on defects and taking dynamic environmental conditions into consideration to predict and prevent latent damages. By reviewing current literature about BDs and drone flight planning, a framework contributing to the automation of drone inspection for precaution and protection of built heritage will be proposed, serving as a contribution to the body of knowledge. As for the contribution to the industry, this research provides a futuristic vision for automatic control of managing real-time BDs and enhances the automated command of UAS and the promotion of digital twin construction for historic buildings through continuous data acquisition and registration.